Descriptions:
How to give an AI agent a real job: start with the customer support problem your team keeps fixing by hand. We closed 51 of 52 support issues, then rebuilt the process so our biggest category stopped happening at all.
How to find the first problem your agent should solve (guide): https://natesnewsletter.substack.com/p/first-ai-agent-use-case?r=1z4sm5&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true
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What’s really happening inside AI automation for customer support?
The common story is that AI helps you answer tickets faster — but the real question is whether the ticket had to exist at all.
In this video, I share the inside scoop on how we used AI to root-cause our support work instead of speeding it up:
– How we took a comparable support week from 52 cases down to 19
– Why you group cases by root cause, not by subject line
– What still needs human approval when access or money is involved
– Where this same repeated pain shows up in sales, finance, and IT
Agents can carry the research and prepare the work, and the cases left over will be the harder ones that still need your judgment.
Chapters:
00:00 Fifty-one of fifty-two support issues, fixed with AI
01:21 The hidden work behind a single support ticket
02:09 Rebuilding the access path so the question stops
05:49 Writing down the process before automating anything
07:34 Recording the pain: one ticket per problem
08:51 Keeping human approval on access and money
09:56 Twenty-six patterns and two upstream failures
11:31 Gumroad: an agent that shipped a bug fix
12:18 When the customer became the approver
14:43 Pull 50 to 100 cases and strip the PII
16:23 Picking a boring, reversible first problem
19:02 Keep a scorecard and count again next week
Listen to this video as a podcast.
Spotify: https://open.spotify.com/show/0gkFdjd1wptEKJKLu9LbZ4
Apple Podcasts: https://podcasts.apple.com/us/podcast/ai-news-strategy-daily-with-nate-b-jones/id1877109372
